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wyh0626

evermemos-mcp-server

by wyh0626

search_memory

Retrieve relevant memories from past sessions using a natural language query, enabling recall of project setup, preferences, and decisions.

Instructions

Search EverMemOS for relevant memories based on a natural language query.

Use this tool when you need to recall past context, such as: project setup details, user preferences, previous decisions, coding patterns, deployment steps, etc.

Args: query: Natural language search query describing what you're looking for. user_id: User ID to search memories for. Defaults to EVERMEM_USER_ID env var. group_id: Optional project/group filter to narrow search scope. retrieve_method: Search strategy - "keyword" (BM25, default), "vector" (semantic), "hybrid" (keyword+vector+rerank, requires rerank service), "rrf" (fusion), "agentic" (LLM-guided multi-round). top_k: Maximum number of results to return (1-20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
user_idNo
group_idNo
retrieve_methodNokeyword

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It explains retrieve_method options but does not disclose behavioral traits like read-only nature, rate limits, result ordering, or whether it modifies state.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a clear header and Args section, but some introductory sentences could be more concise. Overall, it is efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters and an output schema exists, the description adequately covers parameter usage and defaults. It is complete enough for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the tool description provides detailed explanations for all 5 parameters, including the various retrieve_method strategies and default values, adding significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Search EverMemOS for relevant memories based on a natural language query' and lists specific use cases like project setup and user preferences, distinguishing it from sibling tools (delete, get, store).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this tool when you need to recall past context' and provides examples, but does not explicitly mention when not to use it or suggest alternatives. However, sibling tools are clearly different operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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